Text Generation
Transformers
Safetensors
Chinese
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pma3
patriot-memory
viper-gaming
hardware
edge
small-language-model
traditional-chinese
conversational
custom_code
Instructions to use patriotmemory-ai/PMA-1.3-Rosa-256M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use patriotmemory-ai/PMA-1.3-Rosa-256M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="patriotmemory-ai/PMA-1.3-Rosa-256M", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("patriotmemory-ai/PMA-1.3-Rosa-256M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use patriotmemory-ai/PMA-1.3-Rosa-256M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "patriotmemory-ai/PMA-1.3-Rosa-256M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "patriotmemory-ai/PMA-1.3-Rosa-256M", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/patriotmemory-ai/PMA-1.3-Rosa-256M
- SGLang
How to use patriotmemory-ai/PMA-1.3-Rosa-256M with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "patriotmemory-ai/PMA-1.3-Rosa-256M" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "patriotmemory-ai/PMA-1.3-Rosa-256M", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "patriotmemory-ai/PMA-1.3-Rosa-256M" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "patriotmemory-ai/PMA-1.3-Rosa-256M", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use patriotmemory-ai/PMA-1.3-Rosa-256M with Docker Model Runner:
docker model run hf.co/patriotmemory-ai/PMA-1.3-Rosa-256M
Download tokenizer.model from patriotmemory-ai/PMA-1.3-Rosa-256M: direct link, hf CLI and curl.
- Browser
- Download file 348 kB
-
https://huggingface.co/patriotmemory-ai/PMA-1.3-Rosa-256M/resolve/main/tokenizer.model
- Command line
-
hf download hf://patriotmemory-ai/PMA-1.3-Rosa-256M/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/patriotmemory-ai/PMA-1.3-Rosa-256M/resolve/main/tokenizer.model
348 kB
- Xet hash:
- a200bb2e93b9c82d12cb53d3b7192bd8a62da9d0968938cb43aeb3c1bb0a1025
- Size of remote file:
- 348 kB
- SHA256:
- 5fcd308745e72093c7387110ac9b5a523a987399203ba09e9608e737301d3d52
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